Machine learning for particle identification & deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN

dc.contributor.advisorDietel, Thomas
dc.contributor.authorViljoen, Christiaan Gerhardus
dc.date.accessioned2020-05-06T02:23:15Z
dc.date.available2020-05-06T02:23:15Z
dc.date.issued2019
dc.date.updated2020-05-06T01:48:48Z
dc.description.abstractThis Masters thesis outlines the application of machine learning techniques, predominantly deep learning techniques, towards certain aspects of particle physics. Its two main aims: particle identification and high energy physics detector simulations are pertinent to research avenues pursued by physicists working with the ALICE (A Large Ion Collider Experiment) Transition Radiation Detector (TRD), within the Large Hadron Collider (LHC) at CERN (The European Organization for Nuclear Research).
dc.identifier.apacitationViljoen, C. G. (2019). <i>Machine learning for particle identification &amp; deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN</i>. (). ,Faculty of Science ,Department of Physics. Retrieved from en_ZA
dc.identifier.chicagocitationViljoen, Christiaan Gerhardus. <i>"Machine learning for particle identification &amp; deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN."</i> ., ,Faculty of Science ,Department of Physics, 2019. en_ZA
dc.identifier.citationViljoen, C.G. 2019. Machine learning for particle identification &amp; deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN. . ,Faculty of Science ,Department of Physics. en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Viljoen, Christiaan Gerhardus AB - This Masters thesis outlines the application of machine learning techniques, predominantly deep learning techniques, towards certain aspects of particle physics. Its two main aims: particle identification and high energy physics detector simulations are pertinent to research avenues pursued by physicists working with the ALICE (A Large Ion Collider Experiment) Transition Radiation Detector (TRD), within the Large Hadron Collider (LHC) at CERN (The European Organization for Nuclear Research). DA - 2019 DB - OpenUCT DP - University of Cape Town KW - Physics LK - https://open.uct.ac.za PY - 2019 T1 - Machine learning for particle identification &amp; deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN TI - Machine learning for particle identification &amp; deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN UR - ER - en_ZA
dc.identifier.urihttps://hdl.handle.net/11427/31781
dc.identifier.vancouvercitationViljoen CG. Machine learning for particle identification &amp; deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN. []. ,Faculty of Science ,Department of Physics, 2019 [cited yyyy month dd]. Available from: en_ZA
dc.language.rfc3066eng
dc.publisher.departmentDepartment of Physics
dc.publisher.facultyFaculty of Science
dc.subjectPhysics
dc.titleMachine learning for particle identification &amp; deep generative models towards fast simulations for the Alice Transition Radiation Detector at CERN
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMSc
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